KafkaConnector

Input

This transformer accepts any feature.

Output

The output of this transformer will vary depending on the action performed. Message data are presented as feature attributes.

After a Receive action, output features represent messages received from the cluster.
After a Send action, output features represent messages sent to the cluster.
After a Get Metadata action, the output feature represents the state of the cluster.
After a Commit action, the output feature represents the committed partition and offset.

Initiator

The incoming feature is output through this port.

Features that cause the operation to fail are output through this port. An fme_rejection_code attribute, having the value ERROR_DURING_PROCESSING, will be added, along with a more descriptive fme_rejection_message attribute which contains more specific details as to the reason for the failure.

Note If a feature comes in to the KafkaConnector already having a value for fme_rejection_code, this value will be removed.

Rejected Feature Handling: can be set to either terminate the translation or continue running when it encounters a rejected feature. This setting is available both as a default FME option and as a workspace parameter.

Authentication

Credential Source

In addition to anonymous access, the KafkaConnector can authenticate with the cluster. Using a web connection integrates best with FME, but in some cases, you may wish to use one of the other sources.

Anonymous - access an Apache Kafka cluster without authenticating
Web Connection - use an Apache Kafka web connection stored in the FME web connections database
Embedded - embed user name and password as parameters in the transformer

Account

Available when the credential source is Web Connection. To create an Apache Kafka connection, click the 'Account' drop-down box and select 'Add Web Connection...'.

The connection can then be managed via Tools -> FME Options... -> Web Connections.

User Name and Password

Available when the credential source is Embedded. A user name and password can be specified directly in the transformer instead of in a web connection.

Security

Security Protocol	Plaintext - Unauthenticated, non-encrypted channel SSL - Encrypted channel SASL Plaintext - Authenticated, non-encrypted channel SASL SSL - Authenticated, encrypted channel
SASL Mechanism	Plain - username/password authentication SCRAM-SHA-256 - SCRAM authentication with SHA-256 hash function SCRAM-SHA-512 - SCRAM authentication with SHA-512 hash function
SSL CA Certificate	When using one of the SSL security protocols, an optional path to an SSL CA certificate for verifying the cluster

Brokers

The list of bootstrap servers (host and port) to connect to. See the “bootstrap.servers” configuration option at https://kafka.apache.org/documentation.html for more information.

Request

Action

The type of operation to perform. Choices are:

Commit
Get Metadata
Receive
Send

Parameters - Receive Action

Topics

The list of topics to receive from. Topics can be entered manually, or selected interactively by clicking the ellipses in each row of the table.

Receive Behavior

Mode

Two message receiving options are available:

Stream: The connector continues to receive messages indefinitely. The translation needs to be terminated in order to stop reception.
Batch: Only a given number of messages are received before the connection is closed. If the end of the partition is reached, the connection is also closed.

Maximum per Second

In Stream mode, this option limits the number of messages that will be received per second. Leaving the field blank will allow the connector to receive as many messages as possible.

Batch Size

In Batch mode, specifies the number of messages to read per batch.

Receive Options

Consumer Group ID	The consumer group the KafkaConnector is part of. For more information about consumer groups, consult the Kafka documentation: https://kafka.apache.org/documentation/#intro_consumers
Starting Offset	A default starting offset can be specified: Earliest: start receiving from the earliest uncommitted offset Latest: start receiving from the latest uncommitted offset Custom: receive from a given partition and offset
Partition	When Starting Offset is set to Custom, specifies the partition to assign the connector to
Offset	When Starting Offset is set to Custom, specifies the offset to start receiving from
Auto Commit	Whether to automatically commit read offsets Yes: automatically commit the read offset periodically, regardless of whether FME processes the message successfully No: do not automatically commit read offsets. Once the message has been processed successfully, another KafkaConnector should be used to commit its offset. The attribute _connector_id will be produced to identify the KafkaConnector that read the message.

Parameters - Send Action

Topics

The list of topics to send to. Topics can be entered manually, or selected interactively by clicking the ellipses in each row of the table.

Send Options

Create Missing Topics	If Yes is selected, topics will be created automatically if they do not exist.
Number of Partitions	When creating topics, new topics will be created with this number of partitions.
Replication Factor	When creating topics, new topics will be created with this replication factor.

Message Options

The "message options" section is available for sending and receiving messages, but some options are only available when sending.

Message Key	The contents to set for the message key. The message key is usually used for partitioning and log compaction. Only available for sending.
Message Value	The actual message data. This must be a string or binary value. Only available for sending.
Message Schema	The message schema type can be either Simple - a single encoded string or binary value Avro - a schema defined using Apache Avro For more information about Avro schemas, see https://avro.apache.org/docs/current/spec.html.
Encoding	For sending, the encoding to use to convert text input value to binary. Numeric attributes will be converted to a text representation of the number before encoding. If fme-binary is selected, strings will be encoded as utf-8, binary attributes will be sent as-is, and other attribute types will not be supported. For receiving, the _key and _value attributes contain the raw bytes received from the server by default. If an encoding is selected, the connector will instead attempt to decode the value of received message to a string using the selected encoding. Available when Simple is selected for Message Schema.
Schema Registry	When using Avro schema, the URL for the schema registry. See https://docs.confluent.io/current/schema-registry/index.html for more information on schema management using Confluent Schema Registry.
Key Schema	The JSON-based Avro schema to use for the message key. Only available for sending.
Value Schema	The JSON-based Avro schema to use for the message value. Only available for sending.

Parameters - Get Metadata Action

Metadata Options

Getting metadata produces only one output attribute: _metadata. This attribute contains JSON-formatted metadata. The format is an aggregation of cluster, broker, topic and partition metadata.

{

"cluster_id": < cluster ID > ,

"controller_broker_id": < controller broker ID > ,

"brokers": [

{

"id": < broker ID > ,

"host": < host > ,

"port": < port >

},

...

],

"topics": [

{

"name": < topic name > ,

"partitions": [

{

"id": < partition ID > ,

"leader_broker_id": < leader broker ID > ,

"replica_broker_ids": [< replica broker ID >, ...],

"in_sync_replicas": [< in-sync replica ID >, ...],

},

...

],

},

...

],

}

Parameters - Commit Action

Topic

The topic for which to set the commit offset.

Send Options

Receiving Connector Identifier	This parameter must be set to the value of the _connector_id attribute produced by the reading KafkaConnector.
Offset	The offset to commit, typically set from the _offset attribute associated with the message.
Partition	The partition to commit, typically set from the _partition attribute associated with the message.

Advanced Metadata, Consumer, and Producer Options

For all actions, it is possible to supply additional advanced options. For sending and receiving, these options are called producer and consumer, respectively.

The options are supplied as a JSON object, and match the options documented at https://docs.confluent.io/current/clients/librdkafka/CONFIGURATION_8md.html. Note that not all options are available for use in the connector.

Example

{

"socket.timeout.ms": 100,

"check.crcs": true

}

If any user-supplied options conflict with those generated internally by the connector, the user-supplied options will be used. For example, FME usually sets "bootstrap.servers" from the Brokers parameter, but if the advanced options contain "bootstrap.servers" as well, that value will be used instead.

Send and Receive Output Attributes

The following attributes can be selected for inclusion on the output features. Each output feature represents a message that was either sent or received.

_key	The key of the message
_value	The value of the message
_len	The length of the message in bytes
_offset	The offset of the message in the partition
_timestamp	The message timestamp from the cluster. Depending on server configuration, this timestamp may have slightly different meanings.
_topic	The topic the message received from or sent to
_partition	The partition the message is stored in
_headers{}.name _headers{}.value	Structured list attributes containing the header keys and values of a received message. Note that headers for sent messages are not available.

How to Set Parameter Values

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

Processing Behavior	Feature-Based
Feature Holding	No
Dependencies	Apache Kafka
Aliases	ApacheKafkaConnector
History	Released FME 2019.0

KafkaConnector

Typical Uses

Usage Notes

Configuration

Input Ports

Output Ports

Parameters

Editing Transformer Parameters

Reference

FME Community